ESI中心理学及相关领域热点论文信息推送2014年第1期（总第15期）
Bibliographic record
Abstract
ESI热点论文指近两年内发表的在近两个月内被引次数高居前千分之一的SCI/SSCI文章，即最近两个月内最受关注的文章。 本期入榜文章是2011年8月至2013年8月发表的文章中，在2013年9月和10月两个月内被引次数排名前千分之一的文章。数据更新时间为2014年2月7日。 本期ESI（Essential Science Indicators）发布精神病学/心理学领域的热点文章67篇，其中首次入榜文章46篇。入榜文章单篇最高被引163次，由宾夕法尼亚大学（University of Pennsylvania）的NELSON LD等人发表在Psychological Science上，标题为“False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant”，关注心理学研究中的假阳性 (False-positive)现象；最低被引2次，由法国艾克斯-马赛大学（Aix-Marseille University）的GRAINGER J等人合作发表在Psychological Science上题为“Transposed-letter effects reveal orthographic processing in baboons”，关于狒狒的正字加工法。首次入榜的46篇中单篇最高被引32次，由德国马尔堡大学（Univ Marburg）的ROMMELSE N等人发表在American Journal of Psychiatry上标题为“Genome-wide analysis of copy number variants in attention deficit hyperactivity disorder: the role of rare variants and duplications at 15q13.3 ”，关于注意缺陷多动障碍（Attention deficit hyperactivity disorder，ADHD）中拷贝数变异的全基因组分析。该领域所有热点文章的详细信息请见附表1（按文章被引次数排列）。 发布神经科学与行为领域热点文章76篇，其中首次入榜文章50篇。入榜文章单篇最高被引149次，由华盛顿大学医学院（Washington Univ, Sch Med）的BARNES KA等人合作发表在NEUROIMAGE上，标题为“Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion”；最低被引4次的文章标题为“Spatially constrained hierarchical parcellation of the brain with resting-state fMRI”，由牛津大学（Univ Oxford）的BEHRENS TEJ等人发表于NEUROIMAGE上。首次入榜文章单篇最高被引147次，由加州大学洛杉矶分校（Univ Calif Los Angeles, Neurogenet Program）的GUPTA AR等人合作发表在NATURE上，标题为“De novo mutations revealed by whole-exome sequencing are strongly associated with autism”，该研究利用全基因组测序（Whole-exome sequencing）揭示自闭症谱系障碍相关的新生突变（De novo mutation），第一次进入热点论文榜便获得147次被引表明该工作在最近引起十分强烈的关注。 此外，美国人脑连接组计划（Human Connectome Project，HCP）的相关研究在本期也有多项工作入榜，分别是2-35、2-65、2-66、2-68、2-69、2-72（详细信息见附表2）。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".